Palmprint Characterization Using Multi-wavelet Transform for Human Identification


The human hand presents the source for a numerous of physiological biometricfeatures, from these are palmprint, hand geometry, finger geometry and the vein pattern onthe dorsum of the hand, are mostly used in many fields for different applications. Lines andpoints are extracted from palms for individual identification in original image or frequencyspace. In this paper, a preprocessing to extract the central part from the input palmprintimage, next a 2-D multi-wavelet transform is used to convert the palmprint image into 16sub-bands, and the texture feature vectors, energy and entropy for each of the 16 sub-bandsis computed and normalized with min-max method for individual identification. Thecorrelation distance is used as a similarity measure. The experimental results point up theeffectiveness of a method in either using low resolution or noisy images